matplotlib-plt.plot用法

文章目录

英语好的直接参考这个网站

matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)将x,y绘制为线条或标记参数：x, y：数据点的水平/垂直坐标。x值是可选的，默认为range（len（y））。通常，这些参数是一维数组。它们也可以是标量，也可以是二维的（在这种情况下，列代表单独的数据集）。这些参数不能作为关键字传递。fmt：格式字符串，格式字符串只是用于快速设置基本行属性的缩写。所有这些以及更多这些都可以通过关键字参数来控制。此参数不能作为关键字传递。data:具有标签数据的对象。如果提供，请提供标签名称以在x和y中绘制其他参数：scalex, scaley：这些参数确定视图限制是否适合数据限制。这些值将传递到autoscale_view**kwargs：kwarg用于指定属性，例如线标签（用于自动图例），线宽，抗锯齿，标记面颜色等如果使用一个plot调用生成多条线，则kwarg应用于所有这些线。点或线节点的坐标由x，y给出，可选参数fmt是定义基本格式（如颜色，标记和线型）的便捷方法
>>> plot(x, y)        # plot x and y using default line style and color
>>> plot(x, y, 'bo')  # plot x and y using blue circle markers
>>> plot(y)           # plot y using x as index array 0..N-1
>>> plot(y, 'r+')     # ditto, but with red plusses可以将Line2D属性用作关键字参数，以更好地控制外观。线属性和fmt可以混合使用。
>>> plot(x, y, 'go--', linewidth=2, markersize=12)
>>> plot(x, y, color='green', marker='o', linestyle='dashed',
...      linewidth=2, markersize=12)
当与fmt发生冲突时，关键字参数优先有一种方便的方法可以绘制带有标签数据（即可以通过索引obj ['y']访问的数据）的对象。
您可以在data参数中提供对象，而不必为x和y提供数据，而只需为x和y提供标签：
>>> plot('xlabel', 'ylabel', data=obj)
支持所有可索引对象。例如是字典，pandas.DataFrame或结构化的numpy数组。绘制多组数据
有多种方式绘制多组数据
第一种：最直接的方法就是多次调用plot
>>> plot(x1, y1, 'bo')
>>> plot(x2, y2, 'go')
第二种方式：如果您的数据已经是2d数组，则可以将其直接传递给x，y。将为每一列绘制一个单独的数据集。
示例：一个数组a，其中第一列表示x值，其他列为y列：
>>> plot(a[0], a[1:])
第三种方式：指定[x]、y、[fmt]组的多个集合
>>> plot(x1, y1, 'g^', x2, y2, 'g-')

在jupyter notebook中，可以使用plt.plot?来打印

Signature: plt.plot(*args, scalex=True, scaley=True, data=None, **kwargs)
Docstring:
Plot y versus x as lines and/or markers.Call signatures::plot([x], y, [fmt], *, data=None, **kwargs)plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)The coordinates of the points or line nodes are given by *x*, *y*.The optional parameter *fmt* is a convenient way for defining basic
formatting like color, marker and linestyle. It's a shortcut string
notation described in the *Notes* section below.>>> plot(x, y)        # plot x and y using default line style and color
>>> plot(x, y, 'bo')  # plot x and y using blue circle markers
>>> plot(y)           # plot y using x as index array 0..N-1
>>> plot(y, 'r+')     # ditto, but with red plussesYou can use `.Line2D` properties as keyword arguments for more
control on the appearance. Line properties and *fmt* can be mixed.
The following two calls yield identical results:>>> plot(x, y, 'go--', linewidth=2, markersize=12)
>>> plot(x, y, color='green', marker='o', linestyle='dashed',
...      linewidth=2, markersize=12)When conflicting with *fmt*, keyword arguments take precedence.**Plotting labelled data**There's a convenient way for plotting objects with labelled data (i.e.
data that can be accessed by index ``obj['y']``). Instead of giving
the data in *x* and *y*, you can provide the object in the *data*
parameter and just give the labels for *x* and *y*::>>> plot('xlabel', 'ylabel', data=obj)All indexable objects are supported. This could e.g. be a `dict`, a
`pandas.DataFame` or a structured numpy array.**Plotting multiple sets of data**There are various ways to plot multiple sets of data.- The most straight forward way is just to call `plot` multiple times.Example:>>> plot(x1, y1, 'bo')>>> plot(x2, y2, 'go')- Alternatively, if your data is already a 2d array, you can pass itdirectly to *x*, *y*. A separate data set will be drawn for everycolumn.Example: an array ``a`` where the first column represents the *x*values and the other columns are the *y* columns::>>> plot(a[0], a[1:])- The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*groups::>>> plot(x1, y1, 'g^', x2, y2, 'g-')In this case, any additional keyword argument applies to alldatasets. Also this syntax cannot be combined with the *data*parameter.By default, each line is assigned a different style specified by a
'style cycle'. The *fmt* and line property parameters are only
necessary if you want explicit deviations from these defaults.
Alternatively, you can also change the style cycle using the
'axes.prop_cycle' rcParam.Parameters
----------
x, y : array-like or scalarThe horizontal / vertical coordinates of the data points.*x* values are optional and default to `range(len(y))`.Commonly, these parameters are 1D arrays.They can also be scalars, or two-dimensional (in that case, thecolumns represent separate data sets).These arguments cannot be passed as keywords.fmt : str, optionalA format string, e.g. 'ro' for red circles. See the *Notes*section for a full description of the format strings.Format strings are just an abbreviation for quickly settingbasic line properties. All of these and more can also becontrolled by keyword arguments.This argument cannot be passed as keyword.data : indexable object, optionalAn object with labelled data. If given, provide the label names toplot in *x* and *y*... note::Technically there's a slight ambiguity in calls where thesecond label is a valid *fmt*. `plot('n', 'o', data=obj)`could be `plt(x, y)` or `plt(y, fmt)`. In such cases,the former interpretation is chosen, but a warning is issued.You may suppress the warning by adding an empty format string`plot('n', 'o', '', data=obj)`.Other Parameters
----------------
scalex, scaley : bool, optional, default: TrueThese parameters determined if the view limits are adapted tothe data limits. The values are passed on to `autoscale_view`.**kwargs : `.Line2D` properties, optional*kwargs* are used to specify properties like a line label (forauto legends), linewidth, antialiasing, marker face color.Example::>>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)>>> plot([1,2,3], [1,4,9], 'rs',  label='line 2')If you make multiple lines with one plot command, the kwargsapply to all those lines.Here is a list of available `.Line2D` properties:agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) arrayalpha: floatanimated: boolantialiased or aa: boolclip_box: `.Bbox`clip_on: boolclip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]color or c: colorcontains: callabledash_capstyle: {'butt', 'round', 'projecting'}dash_joinstyle: {'miter', 'round', 'bevel'}dashes: sequence of floats (on/off ink in points) or (None, None)drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'figure: `.Figure`fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}gid: strin_layout: boollabel: objectlinestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}linewidth or lw: floatmarker: marker stylemarkeredgecolor or mec: colormarkeredgewidth or mew: floatmarkerfacecolor or mfc: colormarkerfacecoloralt or mfcalt: colormarkersize or ms: floatmarkevery: None or int or (int, int) or slice or List[int] or float or (float, float)path_effects: `.AbstractPathEffect`picker: float or callable[[Artist, Event], Tuple[bool, dict]]pickradius: floatrasterized: bool or Nonesketch_params: (scale: float, length: float, randomness: float)snap: bool or Nonesolid_capstyle: {'butt', 'round', 'projecting'}solid_joinstyle: {'miter', 'round', 'bevel'}transform: `matplotlib.transforms.Transform`url: strvisible: boolxdata: 1D arrayydata: 1D arrayzorder: floatReturns
-------
linesA list of `.Line2D` objects representing the plotted data.See Also
--------
scatter : XY scatter plot with markers of varying size and/or color (sometimes also called bubble chart).Notes
-----
**Format Strings**A format string consists of a part for color, marker and line::fmt = '[marker][line][color]'Each of them is optional. If not provided, the value from the style
cycle is used. Exception: If ``line`` is given, but no ``marker``,
the data will be a line without markers.Other combinations such as ``[color][marker][line]`` are also
supported, but note that their parsing may be ambiguous.**Markers**=============    ===============================
character        description
=============    ===============================
``'.'``          point marker
``','``          pixel marker
``'o'``          circle marker
``'v'``          triangle_down marker
``'^'``          triangle_up marker
``'<'``          triangle_left marker
``'>'``          triangle_right marker
``'1'``          tri_down marker
``'2'``          tri_up marker
``'3'``          tri_left marker
``'4'``          tri_right marker
``'s'``          square marker
``'p'``          pentagon marker
``'*'``          star marker
``'h'``          hexagon1 marker
``'H'``          hexagon2 marker
``'+'``          plus marker
``'x'``          x marker
``'D'``          diamond marker
``'d'``          thin_diamond marker
``'|'``          vline marker
``'_'``          hline marker
=============    ===============================**Line Styles**=============    ===============================
character        description
=============    ===============================
``'-'``          solid line style
``'--'``         dashed line style
``'-.'``         dash-dot line style
``':'``          dotted line style
=============    ===============================Example format strings::'b'    # blue markers with default shape'or'   # red circles'-g'   # green solid line'--'   # dashed line with default color'^k:'  # black triangle_up markers connected by a dotted line**Colors**The supported color abbreviations are the single letter codes=============    ===============================
character        color
=============    ===============================
``'b'``          blue
``'g'``          green
``'r'``          red
``'c'``          cyan
``'m'``          magenta
``'y'``          yellow
``'k'``          black
``'w'``          white
=============    ===============================and the ``'CN'`` colors that index into the default property cycle.If the color is the only part of the format string, you can
additionally use any  `matplotlib.colors` spec, e.g. full names
(``'green'``) or hex strings (``'#008000'``).
File:      d:\softwares\anaconda3\lib\site-packages\matplotlib\pyplot.py
Type:      function

matplotlib-plt.plot用法相关推荐

matplotlib.pyplot.plot 用法详解
python matplotlib演示官网 https://matplotlib.org/xkcd/users/pyplot_tutorial.html https://matplotlib.org/ ...
matplotlib plt.plot
实例1 import matplotlib.pyplot as plta = [1, 2, 3, 4] # y 是 a的值,x是各个元素的索引 b = [5, 6, 7, 8]plt.figure(' ...
plt.plot用法
plt.plot(x, y, format_string, **kwargs) 参数说明 x X轴数据,列表或数组,可选 y Y轴数据,列表或数组 format_string 控制曲线的格式字符串, ...
matplotlib.plt.subplot()用法
在matplotlib下,一个Figure对象可以包含多个子图(Axes),可以使用subplot()快速绘制,其调用形式如下: subplot(numRows, numCols, plotNum) ...
python plt legend并排,matplotlib Legend 图例用法
matplotLib Legend添加图例:展示数据的信息用法: legend(): 默认获取各组数据的Label并展示在图框左上角 legend(labels): legend(handles, ...
matplotlib入门之plt.plot折线图跟常用基本函数
目录一.简单折线图二.常用基本函数:plt.xticks,plt.yticks,ply.xlim,plt.ylim,plt.xlabel,plt.ylabel,plt.title,plt.lege ...
python matplot.pyplot.plot() 的用法 plt.plot()（绘制y相对于x的线条和/或标记。）
文章目录 doc from matplotlib.pyplot.py from matplotlib.axes._axes.py 官网说明: 引用 doc from matplotlib.pyplot ...
4.3Python数据处理篇之Matplotlib系列(三)---plt.plot()折线图
目录前言 (一)plt.plot()函数的本质 ==1.说明== ==2.源代码== ==3.展示效果== (二)plt.plot()函数缺省x时 ==1.说明== ==2.源代码== ==3.展示 ...
Python matplotlib 线图(plt.plot())
http://matplotlib.org/api/lines_api.html#matplotlib.lines.Line2D 线图生成线图对象Line2D Bases: matplotlib.a ...
python中 import matplotlib.pyplot as plt plt.plot 的使用
python中 import matplotlib.pyplot as plt plt.plot 的使用我遇到的问题: 给定一个列表,列表中嵌套了多个列表 lg:b = [[81, 0], [81, ...

matplotlib-plt.plot用法

文章目录

matplotlib-plt.plot用法相关推荐

最新文章

热门文章